Conditional Coverage

Conditional coverage in conformal prediction aims to improve the reliability of prediction intervals by ensuring that the specified coverage probability holds not just on average (marginal coverage), but also for specific subsets of data points defined by their features. Current research focuses on developing algorithms that achieve this stronger guarantee, often leveraging techniques like quantile regression, kernel density estimation, and adaptive thresholding within conformal prediction frameworks. These advancements are crucial for high-stakes applications where reliable uncertainty quantification is paramount, improving the trustworthiness of predictions in diverse fields such as healthcare and finance.